Objects having different sizes can be detected by performing a raster scan on an image obtained by image capturing using a camera, while repeating an image reduction process. FIG. 14 is a schematic diagram showing an object detection process using a raster scan. In FIG. 14, for example, a feature extraction and detection process is repeatedly performed while performing a raster scan on an image 200 in which a person 100 is shown as a detection object, using a scan window 300 having a fixed size. When the raster scan is performed on the image 200 using the scan window 300, an image reduction process can be performed, and thus it is possible to reduce the relative size of the person 100 with respect to the size of the scan window 300. As a result of this process, it is possible to detect objects having different scales (for example, a large person and a small person shown in the image).
One of object detection methods using a raster scan has been a method of extracting a feature value for each local region within a scan window. FIGS. 15(a) to 15(c) are diagrams showing an example of a feature value calculation process using the scan window 300 which is divided into 3×4 local regions. Meanwhile, in these diagrams, it is assumed that (x, y) is x, y coordinates in an input image, S is a scan step, and vertical and horizontal lengths of one local region are S. In this case, first, as shown in (a), a feature value is calculated with respect to all local regions of the scan window 300 at the position of (x, y)=(A, B). Subsequently, as shown in (b), the scan window 300 is advanced by one step in an x direction, and a feature value is calculated for new local regions (four local regions for one vertical row equivalent to the right end within the scan window 300) in the scan window 300 at the position of (x, y)=(A+S, B). With respect to a feature value for the remaining local regions (eight local regions for two vertical rows equivalent to the left end and the center within the scan window 300) of the scan window 300, the feature value calculated one step before can be reused. Similarly, as shown in (c), the scan window 300 is advanced again by one step in the x direction, and only feature values for new local regions in the scan window 300 at the position of (x, y)=(A+2S, B) is calculated. In addition, with respect to feature values for the remaining local regions, the feature value calculated one step before is reused. The above-described process is performed by advancing one step at a time. It is not necessary to recalculate the feature values for the local regions which are calculated one step before by making the scan step conform to the size of the local region, and thus it is possible to reduce the amount of processing in the calculation of the feature value for each step.
Meanwhile, a method of detecting a moving object from a captured image includes an on-image moving object measurement point determination method disclosed in Patent Literature 1. In the on-image moving object measurement point determination method disclosed in Patent Literature 1, an on-image moving object tracking method includes dividing each of time-series images stored in a storage into a plurality of blocks, and identifying a moving object included in a frame image at time t2 in units of blocks and obtaining a motion vector of the moving object in units of blocks on the basis of a correlation between a frame image at time t1 and the frame image at the time t2 and identification results of a moving object included in the frame image at the time t1. The on-image moving object tracking method has a step of (b) obtaining a geometrical centroid of a region of a moving object as an initial representative point, and (c) obtaining a trajectory of the representative point of the region of the moving object by sequentially and cumulatively adding a representative motion vector of the region of the moving object, which are obtained for each of the subsequent frame images, to the initial representative point.